y variable defined on the left remains the same
The p-value of the test is less than the significance level alpha = 0.05. We can conclude that hr sleep and stress sleep are significantly correlated with a correlation coefficient beetween 0.64–0.88.
The p-value of the test is more than the significance level alpha = 0.05. We can conclude that variable are not significantly correlated .
The p-value of the test is more than the significance level alpha = 0.05. We can conclude that variable are not significantly correlated .
The p-value of the test is more than the significance level alpha = 0.05. We can conclude that variable are not significantly correlated .
The p-value of the test is less (2 participants) than the significance level alpha = 0.05. We can conclude that variable are significantly correlated .
---
title: "Sleep Dashboard "
output:
flexdashboard::flex_dashboard:
orientation: rows
vertical_layout: scroll
theme:
bg: "#101010"
fg: "#f3f6f4"
primary: "#ED79F9"
logo: logo.png
source_code: embed
---
```{r setup, include=FALSE}
library(flexdashboard)
library(readr)
library(tidyverse)
library(hrbrthemes)
library(viridis)
library(lubridate)
library(reshape2)
library(ggplot2)
library(ggpubr)
library(plotly)
library(GGally)
setwd("~/Desktop/module_3/all_data")
Lorena <- read_csv("Lorena_converted (3).csv")
Lorena$ID<-"W1"
Martin <- read_csv2("Martin.csv")
Martin$ID<-"M1"
Nely <- read_csv2("Nely.csv")
Nely$ID<-"W2"
dd<-merge(Nely, Martin, all = TRUE)
dd<-merge(dd, Lorena, all = TRUE)
```
Overview
=======================================================================
Row
-----------------------------------------------------------------------
### Global sleep overview (3 subjects Night Owl Early Bird Sleepy Bear)
```{r}
dd1<-dd[,c(1,11,12,13,14,98)]
dd1<-as.data.frame(dd1)
dd1<-melt(dd1, id=c("calendar_date","ID"))
p<-ggplot(dd1, aes(x=calendar_date,y=round(value*100),group=variable, fill=variable)) +
geom_density(stat = "identity",alpha=.6)+theme_ipsum() +scale_fill_ipsum()+
labs(x = "Date",
y = "Sleep in %",
color = "Sleep variable")+facet_grid(~ID)
ggplotly(p)%>% layout(legend = list(orientation = "h", y = -0.2))
```
>y variable defined on the left remains the same
Row
-----------------------------------------------------------------------
### total sleep overview
```{r }
p<-ggplot(dd, aes(x = calendar_date, y = total_sleep_hours)) +
geom_line()+geom_point()+geom_smooth()+theme_ipsum()+facet_grid(~ID)
ggplotly(p)%>% layout(legend = list(orientation = "h", y = -0.2))
```
Row
-----------------------------------------------------------------------
### total sleep overview (by day of week)
```{r }
dd$day<-weekdays(dd$calendar_date,abbreviate = TRUE)
dd$day<-as.factor(dd$day)
dd$day2<-dd$day
levels(dd$day2) <- c("Sunday","Thursday", "Monday", "Tuesday", "Wednesday", "Saturday",
"Friday")
dd$day2 <- ordered(dd$day2, levels =c("Monday","Tuesday", "Wednesday","Thursday", "Friday", "Saturday", "Sunday"))
p<-dd %>%
ggplot( aes(x=day2,y = total_sleep_hours,fill=ID)) +
geom_boxplot() + theme_ipsum() +scale_fill_ipsum()
p %>%
ggplotly() %>%
layout(boxmode = "group")
```
### deep sleep variation (by day of week)
```{r }
p<-dd %>%
ggplot( aes(x=day2,y = deep_sleep_percentage,fill=ID)) +
geom_boxplot() + theme_ipsum() +scale_fill_ipsum()
p %>%
ggplotly() %>%
layout(boxmode = "group")
```
Row
-----------------------------------------------------------------------
### _stress_awake (by day of week)
```{r }
p<-dd %>%
ggplot( aes(x=day2,y = dd$`%_stress_awake`,fill=ID)) +
geom_boxplot() + theme_ipsum() +scale_fill_ipsum()
p %>%
ggplotly() %>%
layout(boxmode = "group")
```
### _stress_awake
```{r }
p<-ggplot(dd, aes(x=calendar_date,y=round(median_stress_awake))) +
geom_smooth()+facet_grid(~ID)+theme_ipsum()
ggplotly(p)
```
Corr_Night analysis
=======================================================================
Row
-----------------------------------------------------------------------
### **Influence from Median_hr_sleep on sleep quality**
```{r }
p<- ggplot(dd,aes(x=perceived_sleep_quality, y=median_hr_sleep, fill=factor(ID))) +
geom_boxplot() +geom_point()+
scale_fill_ipsum()+
labs(x = "perceived_sleep_quality by subject",
y = "median_hr_sleep",
color = "Sleep variable")+ theme_ipsum() +scale_fill_ipsum()
ggplotly(p)%>%
layout(boxmode = "group")
```
### **Influence from median_stress_sleep on sleep quality**
```{r}
p<- ggplot(dd,aes(x=perceived_sleep_quality, y=median_stress_sleep, fill=factor(perceived_sleep_quality))) +
geom_boxplot() +geom_point()+
scale_fill_ipsum()+
labs(x = "perceived_sleep_quality",
y = "median_stress_sleep",
color = "Sleep variable")+ theme_ipsum() +scale_fill_ipsum()
ggplotly(p)%>% layout(legend = list(orientation = "h", y = -0.2))
```
Row
-----------------------------------------------------------------------
### **Correlation stress & HR**
```{r}
p<-ggscatter(dd, x = "median_stress_sleep", y = "median_hr_sleep", color="ID",
add = "reg.line", conf.int = FALSE,
cor.coef = FALSE, cor.method = "pearson")+
stat_cor(aes(color = ID), p.accuracy = 0.001, r.accuracy = 0.01)+ theme_ipsum()+scale_fill_ipsum()
p
```
> The p-value of the test is less than the significance level alpha = 0.05. We can conclude that hr sleep and stress sleep are significantly correlated with a correlation coefficient beetween 0.64--0.88.
### **Correlation vigorous_activity_hours & total_sleep_hours**
```{r}
p<-ggscatter(dd, x = "vigorous_activity_hours", y = "total_sleep_hours", color="ID",
add = "reg.line", conf.int = FALSE,
cor.coef = FALSE, cor.method = "pearson")+
stat_cor(aes(color = ID), p.accuracy = 0.001, r.accuracy = 0.01)+ theme_ipsum()+scale_fill_ipsum()
p
```
>The p-value of the test is more than the significance level alpha = 0.05. We can conclude that variable are not significantly correlated .
### **Correlation steps & total_sleep_hours**
```{r}
p<-ggscatter(dd, x = "steps", y = "total_sleep_hours", color="ID",
add = "reg.line", conf.int = FALSE,
cor.coef = FALSE, cor.method = "pearson")+
stat_cor(aes(color = ID), p.accuracy = 0.001, r.accuracy = 0.01)+ theme_ipsum()+scale_fill_ipsum()
p
```
>The p-value of the test is more than the significance level alpha = 0.05. We can conclude that variable are not significantly correlated .
Row
-----------------------------------------------------------------------
### **Correlation %_high_stress_30min_before_bedtime & total_sleep_hours**
```{r}
p<-ggscatter(dd, x = "%_high_stress_30min_before_bedtime", y = "total_sleep_hours", color="ID",
add = "reg.line", conf.int = FALSE,
cor.coef = FALSE, cor.method = "pearson")+
stat_cor(aes(color = ID), p.accuracy = 0.001, r.accuracy = 0.01)+ theme_ipsum()+scale_fill_ipsum()
p
```
>The p-value of the test is more than the significance level alpha = 0.05. We can conclude that variable are not significantly correlated .
### **Correlation estimated_sleep_time & total_sleep_hours**
```{r}
p<-ggscatter(dd, x = "estimated_sleep_time", y = "total_sleep_hours", color="ID",
add = "reg.line", conf.int = FALSE,
cor.coef = FALSE, cor.method = "pearson")+
stat_cor(aes(color = ID), p.accuracy = 0.001, r.accuracy = 0.01)+ theme_ipsum()+scale_fill_ipsum()
p
```
>The p-value of the test is less (2 participants) than the significance level alpha = 0.05. We can conclude that variable are significantly correlated .
Global corr analysis
=======================================================================
Row
-----------------------------------------------------------------------
### **Correlation **
```{r }
dd2<-dd[,c(6,42,69,97)]
ggpairs(dd2)
```
Row {}
-----------------------------------------------------------------------
### **Comparaison beetween Carmon and oura Ring**
```{r}
```